In [1]:
In [2]:
In [3]:
Out[3]:
User_ID Product_ID Gender Age Occupation City_Category Stay_In_Current_City_Years Marital_Status Product_Category_1 Product_Category_2 Product_Category_3 Purchase
0 1000001 P00069042 F 0-17 10 A 2 0 3 NaN NaN 8370
1 1000001 P00248942 F 0-17 10 A 2 0 1 6.0 14.0 15200
2 1000001 P00087842 F 0-17 10 A 2 0 12 NaN NaN 1422
3 1000001 P00085442 F 0-17 10 A 2 0 12 14.0 NaN 1057
4 1000002 P00285442 M 55+ 16 C 4+ 0 8 NaN NaN 7969
In [4]:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 537577 entries, 0 to 537576
Data columns (total 12 columns):
 #   Column                      Non-Null Count   Dtype  
---  ------                      --------------   -----  
 0   User_ID                     537577 non-null  int64  
 1   Product_ID                  537577 non-null  object 
 2   Gender                      537577 non-null  object 
 3   Age                         537577 non-null  object 
 4   Occupation                  537577 non-null  int64  
 5   City_Category               537577 non-null  object 
 6   Stay_In_Current_City_Years  537577 non-null  object 
 7   Marital_Status              537577 non-null  int64  
 8   Product_Category_1          537577 non-null  int64  
 9   Product_Category_2          370591 non-null  float64
 10  Product_Category_3          164278 non-null  float64
 11  Purchase                    537577 non-null  int64  
dtypes: float64(2), int64(5), object(5)
memory usage: 49.2+ MB
In [5]:
Out[5]:
User_ID                            0
Product_ID                         0
Gender                             0
Age                                0
Occupation                         0
City_Category                      0
Stay_In_Current_City_Years         0
Marital_Status                     0
Product_Category_1                 0
Product_Category_2            166986
Product_Category_3            373299
Purchase                           0
dtype: int64
In [6]:
Out[6]:
User_ID Product_ID Gender Age Occupation City_Category Stay_In_Current_City_Years Marital_Status Product_Category_1 Product_Category_2 Product_Category_3 Purchase
1 1000001 P00248942 F 0-17 10 A 2 0 1 6.0 14.0 15200
6 1000004 P00184942 M 46-50 7 B 2 1 1 8.0 17.0 19215
13 1000005 P00145042 M 26-35 20 A 1 1 1 2.0 5.0 15665
14 1000006 P00231342 F 51-55 9 A 1 0 5 8.0 14.0 5378
16 1000006 P0096642 F 51-55 9 A 1 0 2 3.0 4.0 13055
... ... ... ... ... ... ... ... ... ... ... ... ...
537549 1004734 P00345842 M 51-55 1 B 1 1 2 8.0 14.0 13082
537551 1004735 P00313442 M 46-50 3 C 3 0 5 6.0 8.0 6863
537562 1004736 P00146742 M 18-25 20 A 1 1 1 13.0 14.0 11508
537571 1004737 P00221442 M 36-45 16 C 1 0 1 2.0 5.0 11852
537573 1004737 P00111142 M 36-45 16 C 1 0 1 15.0 16.0 19196

164278 rows × 12 columns

here if we drop then we will left with only 30 per of data that's why we will finally remove the column

In [7]:
In [8]:
Out[8]:
User_ID Product_ID Gender Age Occupation City_Category Stay_In_Current_City_Years Marital_Status Product_Category_1 Purchase
0 1000001 P00069042 F 0-17 10 A 2 0 3 8370
1 1000001 P00248942 F 0-17 10 A 2 0 1 15200
2 1000001 P00087842 F 0-17 10 A 2 0 12 1422
3 1000001 P00085442 F 0-17 10 A 2 0 12 1057
4 1000002 P00285442 M 55+ 16 C 4+ 0 8 7969
... ... ... ... ... ... ... ... ... ... ...
537572 1004737 P00193542 M 36-45 16 C 1 0 1 11664
537573 1004737 P00111142 M 36-45 16 C 1 0 1 19196
537574 1004737 P00345942 M 36-45 16 C 1 0 8 8043
537575 1004737 P00285842 M 36-45 16 C 1 0 5 7172
537576 1004737 P00118242 M 36-45 16 C 1 0 5 6875

537577 rows × 10 columns

Analysing the columns

In [9]:
Out[9]:
0         P00069042
1         P00248942
2         P00087842
3         P00085442
4         P00285442
            ...    
537572    P00193542
537573    P00111142
537574    P00345942
537575    P00285842
537576    P00118242
Name: Product_ID, Length: 537577, dtype: object
In [10]:
Out[10]:
0         1000001
1         1000001
2         1000001
3         1000001
4         1000002
           ...   
537572    1004737
537573    1004737
537574    1004737
537575    1004737
537576    1004737
Name: User_ID, Length: 537577, dtype: int64
In [11]:
Out[11]:
5891
In [12]:
Out[12]:
array([1000001, 1000002, 1000003, ..., 1004113, 1005391, 1001529],
      dtype=int64)
In [13]:
Out[13]:
3623
In [14]:
Out[14]:
array(['P00069042', 'P00248942', 'P00087842', ..., 'P00038842',
       'P00295642', 'P00091742'], dtype=object)
In [15]:
Out[15]:
array(['F', 'M'], dtype=object)
In [16]:
Out[16]:
array(['0-17', '55+', '26-35', '46-50', '51-55', '36-45', '18-25'],
      dtype=object)
In [17]:
Out[17]:
array([10, 16, 15,  7, 20,  9,  1, 12, 17,  0,  3,  4, 11,  8, 19,  2, 18,
        5, 14, 13,  6], dtype=int64)
In [18]:
Out[18]:
array(['A', 'C', 'B'], dtype=object)
In [19]:
Out[19]:
array(['2', '4+', '3', '1', '0'], dtype=object)
In [20]:
Out[20]:
array([0, 1], dtype=int64)
In [21]:
Out[21]:
9333.859852635065
In [22]:
Out[22]:
Index(['User_ID', 'Product_ID', 'Gender', 'Age', 'Occupation', 'City_Category',
       'Stay_In_Current_City_Years', 'Marital_Status', 'Product_Category_1',
       'Purchase'],
      dtype='object')
In [23]:
User_ID 5891
Product_ID 3623
Gender 2
Age 7
Occupation 21
City_Category 3
Stay_In_Current_City_Years 5
Marital_Status 2
Product_Category_1 18
Purchase 17959

Analysing thegender

In [24]:
Out[24]:
User_ID Product_ID Gender Age Occupation City_Category Stay_In_Current_City_Years Marital_Status Product_Category_1 Purchase
4 1000002 P00285442 M 55+ 16 C 4+ 0 8 7969
5 1000003 P00193542 M 26-35 15 A 3 0 1 15227
6 1000004 P00184942 M 46-50 7 B 2 1 1 19215
7 1000004 P00346142 M 46-50 7 B 2 1 1 15854
8 1000004 P0097242 M 46-50 7 B 2 1 1 15686
... ... ... ... ... ... ... ... ... ... ...
537572 1004737 P00193542 M 36-45 16 C 1 0 1 11664
537573 1004737 P00111142 M 36-45 16 C 1 0 1 19196
537574 1004737 P00345942 M 36-45 16 C 1 0 8 8043
537575 1004737 P00285842 M 36-45 16 C 1 0 5 7172
537576 1004737 P00118242 M 36-45 16 C 1 0 5 6875

405380 rows × 10 columns

In [25]:
Out[25]:
{'Ratio': [405380, 132197]}
In [26]:
In [27]:
Out[27]:
Ratio
0 405380
1 132197
In [28]:
Out[28]:
<Axes: >
In [29]:
Out[29]:
<Axes: ylabel='Ratio'>
In [30]:
Out[30]:
<Axes: ylabel='Ratio'>
In [31]:
Out[31]:
<Axes: xlabel='Gender'>
In [32]:
Out[32]:
Gender
F    132197
M    405380
dtype: int64
In [33]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\978566082.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Gender').sum()['Purchase']
Out[33]:
Gender
F    1164624021
M    3853044357
Name: Purchase, dtype: int64
In [34]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\262630123.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Gender').sum()['Purchase'].plot(kind='pie',autopct='%1.2f%%')
Out[34]:
<Axes: ylabel='Purchase'>
In [35]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\1835943715.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Gender').mean()['Purchase'].plot(kind='pie',autopct='%0.1f%%')
Out[35]:
<Axes: ylabel='Purchase'>

Analysing Age & marital Status

In [36]:
Out[36]:
Age
0-17      14707
18-25     97634
26-35    214690
36-45    107499
46-50     44526
51-55     37618
55+       20903
dtype: int64
In [37]:
Out[37]:
<Axes: title={'center': 'Purchased distribution'}, xlabel='Age'>
In [38]:
0-17
55+
26-35
46-50
51-55
36-45
18-25
In [39]:
Out[39]:
User_ID Product_ID Gender Age Occupation City_Category Stay_In_Current_City_Years Marital_Status Product_Category_1 Purchase
0 1000001 P00069042 F 0-17 10 A 2 0 3 8370
1 1000001 P00248942 F 0-17 10 A 2 0 1 15200
2 1000001 P00087842 F 0-17 10 A 2 0 12 1422
3 1000001 P00085442 F 0-17 10 A 2 0 12 1057
85 1000019 P00112542 M 0-17 10 A 3 0 1 7746
... ... ... ... ... ... ... ... ... ... ...
537280 1004690 P00242742 F 0-17 10 C 3 0 1 11561
537281 1004690 P00226442 F 0-17 10 C 3 0 11 6010
537282 1004690 P00181442 F 0-17 10 C 3 0 11 7458
537283 1004690 P00192042 F 0-17 10 C 3 0 5 3706
537354 1004707 P00253042 M 0-17 0 C 4+ 0 8 2304

14707 rows × 10 columns

In [40]:
Out[40]:
14707
In [41]:
0-17 : 14707
55+ : 20903
26-35 : 214690
46-50 : 44526
51-55 : 37618
36-45 : 107499
18-25 : 97634
In [42]:
Out[42]:
array(['P00366542', 'P00190742', 'P00151842', ..., 'P00195142',
       'P00091742', 'P00091042'], dtype=object)
In [43]:
Out[43]:
3213
In [44]:
0-17 : 2300
55+ : 2573
26-35 : 3419
46-50 : 3099
51-55 : 2877
36-45 : 3318
18-25 : 3213
In [45]:
In [46]:
Out[46]:
<Axes: >
In [47]:
Out[47]:
Age Products
0 0-17 2300
1 55+ 2573
2 26-35 3419
3 46-50 3099
4 51-55 2877
5 36-45 3318
6 18-25 3213
In [48]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\2731425115.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Age').sum()['Purchase'].plot(kind='bar',figsize=(12,6),title='Amount Spend by Age')
Out[48]:
<Axes: title={'center': 'Amount Spend by Age'}, xlabel='Age'>
In [49]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\1281252908.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Age').mean()['Purchase'].plot(kind='bar',figsize=(12,6),title='Amount Spend by Age')
Out[49]:
<Axes: title={'center': 'Amount Spend by Age'}, xlabel='Age'>
In [50]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\3630277726.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Age').mean()['Purchase']
Out[50]:
Age
0-17     9020.126878
18-25    9235.197575
26-35    9314.588970
36-45    9401.478758
46-50    9284.872277
51-55    9620.616620
55+      9453.898579
Name: Purchase, dtype: float64
In [51]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\3389442800.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Age').mean()['Purchase'].plot(kind='pie',autopct='%.1f%%')
Out[51]:
<Axes: ylabel='Purchase'>
In [52]:
Out[52]:
<Axes: >
In [53]:
Out[53]:
<Axes: >

Multicolumn analysing

In [54]:
Out[54]:
<Axes: xlabel='Age', ylabel='count'>
In [55]:
Out[55]:
<Axes: xlabel='Gender', ylabel='count'>
In [56]:
Out[56]:
<Axes: xlabel='Marital_Status', ylabel='count'>
In [57]:
Out[57]:
<Axes: xlabel='Gender', ylabel='count'>
In [58]:
Out[58]:
<Axes: xlabel='Gender', ylabel='count'>

We can plot single also

In [59]:
Out[59]:
<Axes: xlabel='City_Category', ylabel='count'>
In [60]:
Out[60]:
<Axes: >
In [61]:
Out[61]:
<Axes: xlabel='City_Category', ylabel='count'>
In [62]:
Out[62]:
<Axes: xlabel='City_Category', ylabel='count'>
In [63]:
Out[63]:
<Axes: xlabel='Gender', ylabel='count'>
In [64]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\1888884886.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby ('City_Category').sum()['Purchase']
Out[64]:
City_Category
A    1295668797
B    2083431612
C    1638567969
Name: Purchase, dtype: int64
In [65]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\3745041134.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby ('City_Category').sum()['Purchase'].plot(kind='bar')
Out[65]:
<Axes: xlabel='City_Category'>
In [66]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\2165780092.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby ('City_Category').sum()['Purchase'].plot(kind='pie',autopct='%.1f%%')
Out[66]:
<Axes: ylabel='Purchase'>
In [67]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\2710379268.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby ('City_Category').mean()['Purchase'].plot(kind='pie',autopct='%.1f%%')
Out[67]:
<Axes: ylabel='Purchase'>

see if we are seeing that in respect to mean both A and b are same

occupation and products Analysis

In [68]:
Out[68]:
<Axes: xlabel='Stay_In_Current_City_Years', ylabel='count'>
In [69]:
Out[69]:
<Axes: xlabel='Stay_In_Current_City_Years', ylabel='count'>
In [70]:
Out[70]:
<Axes: xlabel='Stay_In_Current_City_Years', ylabel='count'>
In [71]:
Out[71]:
<Axes: xlabel='Stay_In_Current_City_Years', ylabel='count'>
In [72]:
Out[72]:
<Axes: xlabel='City_Category', ylabel='count'>
In [73]:
Out[73]:
<Axes: >
In [74]:
Out[74]:
<Axes: xlabel='City_Category', ylabel='count'>
In [75]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\1647040737.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Stay_In_Current_City_Years').sum()['Purchase'].plot(kind='pie',autopct='%.1f%%')
Out[75]:
<Axes: ylabel='Purchase'>
In [76]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\4105308664.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Stay_In_Current_City_Years').sum()['Purchase'].plot(kind='bar')
Out[76]:
<Axes: xlabel='Stay_In_Current_City_Years'>
In [77]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\2310944924.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Stay_In_Current_City_Years').mean()['Purchase'].plot(kind='bar')
Out[77]:
<Axes: xlabel='Stay_In_Current_City_Years'>
In [78]:
Out[78]:
<Axes: >
In [79]:
Out[79]:
<Axes: xlabel='Occupation', ylabel='count'>
In [80]:
Out[80]:
<Axes: xlabel='Occupation'>
In [81]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\1476192230.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Occupation').sum()['Purchase'].sort_values().plot(kind='bar')
Out[81]:
<Axes: xlabel='Occupation'>
In [82]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\1830348976.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Occupation').mean()['Purchase'].sort_values().plot(kind='bar')
Out[82]:
<Axes: xlabel='Occupation'>
In [83]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\3915541376.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Occupation').mean()['Purchase'].sort_values()
Out[83]:
Occupation
9     8714.335934
19    8754.249162
20    8881.099514
1     9017.703095
2     9025.938982
10    9052.836410
0     9186.946726
18    9233.671418
3     9238.077277
4     9279.026742
11    9299.467190
6     9336.378620
5     9388.848978
13    9424.449391
16    9457.133118
7     9502.175276
14    9568.536426
8     9576.508530
15    9866.239925
12    9883.052460
17    9906.378997
Name: Purchase, dtype: float64
In [84]:
Out[84]:
<Axes: xlabel='Occupation', ylabel='count'>
In [85]:
Out[85]:
<Axes: xlabel='Occupation', ylabel='count'>
In [86]:
Out[86]:
<Axes: xlabel='Occupation', ylabel='count'>
In [87]:
Out[87]:
<Axes: xlabel='Occupation'>
In [88]:
Out[88]:
<Axes: >
In [89]:
Out[89]:
User_ID Product_ID Gender Age Occupation City_Category Stay_In_Current_City_Years Marital_Status Product_Category_1 Purchase
0 1000001 P00069042 F 0-17 10 A 2 0 3 8370
1 1000001 P00248942 F 0-17 10 A 2 0 1 15200
2 1000001 P00087842 F 0-17 10 A 2 0 12 1422
3 1000001 P00085442 F 0-17 10 A 2 0 12 1057
4 1000002 P00285442 M 55+ 16 C 4+ 0 8 7969
... ... ... ... ... ... ... ... ... ... ...
537572 1004737 P00193542 M 36-45 16 C 1 0 1 11664
537573 1004737 P00111142 M 36-45 16 C 1 0 1 19196
537574 1004737 P00345942 M 36-45 16 C 1 0 8 8043
537575 1004737 P00285842 M 36-45 16 C 1 0 5 7172
537576 1004737 P00118242 M 36-45 16 C 1 0 5 6875

537577 rows × 10 columns

In [90]:
Out[90]:
<Axes: >
In [91]:
Out[91]:
<Axes: xlabel='Product_Category_1'>
In [92]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\2889786962.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Product_Category_1').sum()['Purchase'].sort_values().plot(kind='pie')
Out[92]:
<Axes: ylabel='Purchase'>
In [93]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\594745959.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Product_Category_1').sum()['Purchase'].sort_values().plot(kind='bar')
Out[93]:
<Axes: xlabel='Product_Category_1'>
In [94]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\224135489.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Product_Category_1').mean()['Purchase'].sort_values().plot(kind='bar')
Out[94]:
<Axes: xlabel='Product_Category_1'>
In [95]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\1005973657.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Product_ID').sum()['Purchase'].nlargest(10).sort_values().plot(kind='bar')
Out[95]:
<Axes: xlabel='Product_ID'>
In [96]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\940603466.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Product_ID').mean()['Purchase'].nlargest(10).sort_values().plot(kind='bar')
Out[96]:
<Axes: xlabel='Product_ID'>
In [97]:
C:\Users\Asus\AppData\Local\Temp\ipykernel_22856\940603466.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df.groupby('Product_ID').mean()['Purchase'].nlargest(10).sort_values().plot(kind='bar')
Out[97]:
<Axes: xlabel='Product_ID'>
In [98]:
Out[98]:
<Axes: xlabel='Product_Category_1', ylabel='count'>
In [99]:
Out[99]:
<Axes: xlabel='Product_Category_1', ylabel='count'>

combining all the things

In [100]:
F_0
F_0
F_0
F_0
M_0
M_0
M_1
M_1
M_1
M_1
M_1
M_1
M_1
M_1
F_0
F_0
F_0
F_0
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In [ ]:
In [105]:
Out[105]:
User_ID Product_ID Gender Age Occupation City_Category Stay_In_Current_City_Years Marital_Status Product_Category_1 Purchase
0 1000001 P00069042 F 0-17 10 A 2 0 3 8370
1 1000001 P00248942 F 0-17 10 A 2 0 1 15200
2 1000001 P00087842 F 0-17 10 A 2 0 12 1422
3 1000001 P00085442 F 0-17 10 A 2 0 12 1057
4 1000002 P00285442 M 55+ 16 C 4+ 0 8 7969

Fix the issue of the above

In [108]:
In [109]:
Out[109]:
User_ID Product_ID Gender Age Occupation City_Category Stay_In_Current_City_Years Marital_Status Product_Category_1 Purchase MaritalGender
0 1000001 P00069042 F 0-17 10 A 2 0 3 8370 F_0
1 1000001 P00248942 F 0-17 10 A 2 0 1 15200 F_0
2 1000001 P00087842 F 0-17 10 A 2 0 12 1422 F_0
3 1000001 P00085442 F 0-17 10 A 2 0 12 1057 F_0
4 1000002 P00285442 M 55+ 16 C 4+ 0 8 7969 M_0
In [110]:
Out[110]:
<Axes: xlabel='MaritalGender', ylabel='count'>
In [112]:
Out[112]:
<Axes: xlabel='Age', ylabel='count'>
In [113]:
Out[113]:
<Axes: xlabel='Product_Category_1', ylabel='count'>
In [114]:
Out[114]:
<Axes: xlabel='Stay_In_Current_City_Years', ylabel='count'>
In [115]:
Out[115]:
<Axes: xlabel='City_Category', ylabel='count'>
In [116]:
In [121]:
1.5.3
In [127]:
Requirement already satisfied: pandas-profiling in c:\users\asus\anaconda3\lib\site-packages (3.6.6)
Requirement already satisfied: ydata-profiling in c:\users\asus\anaconda3\lib\site-packages (from pandas-profiling) (4.2.0)
Requirement already satisfied: scipy<1.11,>=1.4.1 in c:\users\asus\anaconda3\lib\site-packages (from ydata-profiling->pandas-profiling) (1.10.0)
Requirement already satisfied: imagehash==4.3.1 in c:\users\asus\anaconda3\lib\site-packages (from ydata-profiling->pandas-profiling) (4.3.1)
Requirement already satisfied: phik<0.13,>=0.11.1 in c:\users\asus\anaconda3\lib\site-packages (from ydata-profiling->pandas-profiling) (0.12.3)
Requirement already satisfied: wordcloud>=1.9.1 in c:\users\asus\anaconda3\lib\site-packages (from ydata-profiling->pandas-profiling) (1.9.2)
Requirement already satisfied: pydantic<2,>=1.8.1 in c:\users\asus\anaconda3\lib\site-packages (from ydata-profiling->pandas-profiling) (1.10.8)
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Requirement already satisfied: visions[type_image_path]==0.7.5 in c:\users\asus\anaconda3\lib\site-packages (from ydata-profiling->pandas-profiling) (0.7.5)
Requirement already satisfied: jinja2<3.2,>=2.11.1 in c:\users\asus\anaconda3\lib\site-packages (from ydata-profiling->pandas-profiling) (3.1.2)
Requirement already satisfied: matplotlib<4,>=3.2 in c:\users\asus\anaconda3\lib\site-packages (from ydata-profiling->pandas-profiling) (3.7.0)
Requirement already satisfied: htmlmin==0.1.12 in c:\users\asus\anaconda3\lib\site-packages (from ydata-profiling->pandas-profiling) (0.1.12)
Requirement already satisfied: numpy<1.24,>=1.16.0 in c:\users\asus\anaconda3\lib\site-packages (from ydata-profiling->pandas-profiling) (1.23.5)
Requirement already satisfied: pandas!=1.4.0,<2,>1.1 in c:\users\asus\anaconda3\lib\site-packages (from ydata-profiling->pandas-profiling) (1.5.3)
Requirement already satisfied: dacite>=1.8 in c:\users\asus\anaconda3\lib\site-packages (from ydata-profiling->pandas-profiling) (1.8.1)
Requirement already satisfied: requests<3,>=2.24.0 in c:\users\asus\anaconda3\lib\site-packages (from ydata-profiling->pandas-profiling) (2.28.1)
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Requirement already satisfied: multimethod<2,>=1.4 in c:\users\asus\anaconda3\lib\site-packages (from ydata-profiling->pandas-profiling) (1.9.1)
Requirement already satisfied: tqdm<5,>=4.48.2 in c:\users\asus\anaconda3\lib\site-packages (from ydata-profiling->pandas-profiling) (4.64.1)
Requirement already satisfied: PyYAML<6.1,>=5.0.0 in c:\users\asus\anaconda3\lib\site-packages (from ydata-profiling->pandas-profiling) (6.0)
Requirement already satisfied: PyWavelets in c:\users\asus\anaconda3\lib\site-packages (from imagehash==4.3.1->ydata-profiling->pandas-profiling) (1.4.1)
Requirement already satisfied: pillow in c:\users\asus\anaconda3\lib\site-packages (from imagehash==4.3.1->ydata-profiling->pandas-profiling) (9.4.0)
Requirement already satisfied: networkx>=2.4 in c:\users\asus\anaconda3\lib\site-packages (from visions[type_image_path]==0.7.5->ydata-profiling->pandas-profiling) (2.8.4)
Requirement already satisfied: attrs>=19.3.0 in c:\users\asus\anaconda3\lib\site-packages (from visions[type_image_path]==0.7.5->ydata-profiling->pandas-profiling) (22.1.0)
Requirement already satisfied: tangled-up-in-unicode>=0.0.4 in c:\users\asus\anaconda3\lib\site-packages (from visions[type_image_path]==0.7.5->ydata-profiling->pandas-profiling) (0.2.0)
Requirement already satisfied: MarkupSafe>=2.0 in c:\users\asus\anaconda3\lib\site-packages (from jinja2<3.2,>=2.11.1->ydata-profiling->pandas-profiling) (2.1.1)
Requirement already satisfied: contourpy>=1.0.1 in c:\users\asus\anaconda3\lib\site-packages (from matplotlib<4,>=3.2->ydata-profiling->pandas-profiling) (1.0.5)
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Requirement already satisfied: python-dateutil>=2.7 in c:\users\asus\anaconda3\lib\site-packages (from matplotlib<4,>=3.2->ydata-profiling->pandas-profiling) (2.8.2)
Requirement already satisfied: fonttools>=4.22.0 in c:\users\asus\anaconda3\lib\site-packages (from matplotlib<4,>=3.2->ydata-profiling->pandas-profiling) (4.25.0)
Requirement already satisfied: pytz>=2020.1 in c:\users\asus\anaconda3\lib\site-packages (from pandas!=1.4.0,<2,>1.1->ydata-profiling->pandas-profiling) (2022.7)
Requirement already satisfied: joblib>=0.14.1 in c:\users\asus\anaconda3\lib\site-packages (from phik<0.13,>=0.11.1->ydata-profiling->pandas-profiling) (1.1.1)
Requirement already satisfied: typing-extensions>=4.2.0 in c:\users\asus\anaconda3\lib\site-packages (from pydantic<2,>=1.8.1->ydata-profiling->pandas-profiling) (4.4.0)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\users\asus\anaconda3\lib\site-packages (from requests<3,>=2.24.0->ydata-profiling->pandas-profiling) (1.26.14)
Requirement already satisfied: idna<4,>=2.5 in c:\users\asus\anaconda3\lib\site-packages (from requests<3,>=2.24.0->ydata-profiling->pandas-profiling) (3.4)
Requirement already satisfied: charset-normalizer<3,>=2 in c:\users\asus\anaconda3\lib\site-packages (from requests<3,>=2.24.0->ydata-profiling->pandas-profiling) (2.0.4)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\asus\anaconda3\lib\site-packages (from requests<3,>=2.24.0->ydata-profiling->pandas-profiling) (2022.12.7)
Requirement already satisfied: patsy>=0.5.2 in c:\users\asus\anaconda3\lib\site-packages (from statsmodels<1,>=0.13.2->ydata-profiling->pandas-profiling) (0.5.3)
Requirement already satisfied: colorama in c:\users\asus\anaconda3\lib\site-packages (from tqdm<5,>=4.48.2->ydata-profiling->pandas-profiling) (0.4.6)
Requirement already satisfied: six in c:\users\asus\anaconda3\lib\site-packages (from patsy>=0.5.2->statsmodels<1,>=0.13.2->ydata-profiling->pandas-profiling) (1.16.0)

WARNING: There was an error checking the latest version of pip.
In [131]:
Summarize dataset: 100%
58/58 [01:02<00:00, 1.19s/it, Completed]
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In [132]:
Out[132]:
completed


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